The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.11 - Nov. (2012 vol.34)
pp: 2189-2202
B. Alexe , Comput. Vision Lab., ETH Zurich, Zurich, Switzerland
T. Deselaers , Google Switzerland, Zurich, Switzerland
V. Ferrari , IPAB Inst., Univ. of Edinburgh, Edinburgh, UK
ABSTRACT
We present a generic objectness measure, quantifying how likely it is for an image window to contain an object of any class. We explicitly train it to distinguish objects with a well-defined boundary in space, such as cows and telephones, from amorphous background elements, such as grass and road. The measure combines in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. These include an innovative cue to measure the closed boundary characteristic. In experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to outperform a state-of-the-art saliency measure, and the combined objectness measure to perform better than any cue alone. We also compare to interest point operators, a HOG detector, and three recent works aiming at automatic object segmentation. Finally, we present two applications of objectness. In the first, we sample a small numberof windows according to their objectness probability and give an algorithm to employ them as location priors for modern class-specific object detectors. As we show experimentally, this greatly reduces the number of windows evaluated by the expensive class-specific model. In the second application, we use objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives. As shown in several recent papers, objectness can act as a valuable focus of attention mechanism in many other applications operating on image windows, including weakly supervised learning of object categories, unsupervised pixelwise segmentation, and object tracking in video. Computing objectness is very efficient and takes only about 4 sec. per image.
INDEX TERMS
Detectors, Image edge detection, Image segmentation, Kernel, Image color analysis, Training, Area measurement, object recognition, Objectness measure, object detection
CITATION
B. Alexe, T. Deselaers, V. Ferrari, "Measuring the Objectness of Image Windows", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 11, pp. 2189-2202, Nov. 2012, doi:10.1109/TPAMI.2012.28
REFERENCES
[1] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequency-Tuned Salient Region Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[2] B. Alexe, T. Deselaers, and V. Ferrari, "ClassCut for Unsupervised Class Segmentation," Proc. 11th European Conf. Computer Vision, 2010.
[3] B. Alexe, T. Deselaers, and V. Ferrari, "What Is an Object?" Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[4] J. Arpit, R. Saiprasad, and M. Anurag, "Multi-Stage Contour Based Detection of Deformable Objects," Proc. 11th European Conf. Computer Vision, 2008.
[5] X. Bao, T. Narayan, A.A. Sani, W. Richter, R.R. Choudhury, L. Zhong, and M. Satyanarayanan, "The Case for Context-Aware Compression," Proc. ACM 12th Workshop Mobile Computing Systems and Applications, 2011.
[6] T.L. Berg and A. Berg, "Finding Iconic Images," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2009.
[7] N. Bruce and J. Tsotsos, "Saliency Based on Information Maximization," Proc. Neural Information Processing Systems, 2005.
[8] J.F. Canny, "A Computational Approach to Edge Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, Nov. 1986.
[9] J. Carreira, F. Li, and C. Sminchisescu, "Constrained Parametric Min Cuts for Automatic Object Segmentation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[10] F. Crow, "Summed-Area Tables for Texture Mapping," Proc. ACM Siggraph, 1984.
[11] N. Dalal and B. Triggs, "Histogram of Oriented Gradients for Human Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, 2005.
[12] C. Desai, D. Ramanan, and C. Folkess, "Discriminative Models for Multi-Class Object Layout," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
[13] T. Deselaers, B. Alexe, and V. Ferrari, "Localizing Objects While Learning Their Appearance," Proc. 11th European Conf. Computer Vision, 2010.
[14] R. Desimone and J. Duncan, "Neural Mechanisms of Selective Visual-Attention," Ann. Rev. Neuroscience, vol. 1, no. 18, pp. 193-222, 1995.
[15] W. Einhauser and P. Konig, "Does Luminance-Contrast Contribute to Saliency Map for Overt Visual Attention," European J. Neuroscience, vol. 5, no. 17, pp. 1089-1097, 2003.
[16] I. Endres and D. Hoiem, "Category Independent Object Proposals," Proc. 11th European Conf. Computer Vision, 2010.
[17] M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman, "The PASCAL Visual Object Classes Challenge 2007 Results," 2007.
[18] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, "Object Detection with Discriminatively Trained Part Based Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, Sept. 2010.
[19] P.F. Felzenszwalb and D.P. Huttenlocher, "Efficient Graph-Based Image Segmentation," Int'l J. Computer Vision, vol. 59, no. 2, pp. 167-181, Sept. 2004.
[20] V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, "Groups of Adjacent Contour Segments for Object Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 1, pp. 36-51, Jan. 2008.
[21] D. Gao and N. Vasconcelos, "Bottom-Up Saliency is a Discriminant Process," Proc. 11th IEEE Int'l Conf. Computer Vision, 2007.
[22] S. Gould, R. Fulton, and D. Koller, "Decomposing a Scene into Geometric and Semantically Consistent Regions," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
[23] S. Gould, O. Russakovsky, I. Goodfellow, P. Baumstarck, A. Ng, and D. Koller, The STAIR Vision Library (v2.4), software available at http://ai.stanford.edu/sgouldsvl, 2010.
[24] J. Harel, C. Koch, and P. Perona, "Graph-Based Visual Saliency," Proc. Neural Information Processing Systems, 2007.
[25] H. Harzallah, F. Jurie, and C. Schmid, "Combining Efficient Object Localization and Image Classification," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
[26] G. Heitz and D. Koller, "Learning Spatial Context: Using Stuff to Find Things," Proc. 10th European Conf. Computer Vision, 2008.
[27] X. Hou and L. Zhang, "Saliency Detection: A Spectral Residual Approach," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[28] L. Itti, C. Koch, and E. Niebur, "A Model of Saliency-Based Visual Attention for Rapid Scene Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, Nov. 1998.
[29] T. Kadir, A. Zisserman, and M. Brady, "An Affine Invariant Salient Region Detector," Proc. European Conf. Computer Vision, May 2004.
[30] I. Khan, P.M. Roth, and H. Bischof, "Learning Object Detectors from Weakly-Labeled Internet Images," Proc. OAGM Workshop, 2011.
[31] W. Kienzle, F. Wichmann, A.B. Scholkopf, and M.O. Franz, "A Nonparametric Approach to Bottom-Up Visual Saliency," Proc. Neural Information Processing Systems, 2006.
[32] G. Krieger, I. Rentschler, G. Hauske, K. Schill, and C. Zetzsche, "Object and Scene Analysis by Saccadic Eye-Movements: An Investigation with Higher-Order Statistics," Spatial Vision, vol. 2, no. 16, pp. 201-214, 2000.
[33] C.H. Lampert, M.B. Blaschko, and T. Hofmann, "Beyond Sliding Windows: Object Localization by Efficient Subwindow Search," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[34] Y.J. Lee and K. Grauman, "Learning the Easy Things First: Self-Paced Visual Category Discovery," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
[35] B. Leibe and B. Schiele, "Scale-Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search," Proc. German Assoc. for Pattern Recognition Symp., 2004.
[36] T. Liu, J. Sun, N. Zheng, X. Tang, and H. Shum, "Learning to Detect a Salient Object," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[37] D. Lowe, "Object Recognition from Local Scale-Invariant Features," Proc. Seventh IEEE Int'l Conf. Computer Vision, pp. 1150-1157, Sept. 1999.
[38] Y.F. Ma and H.J. Zhang, "Contrast-Based Image Attention Analysis by Using Fuzzy Growing," Proc. 11th ACM Int'l Conf. Multimedia, 2003.
[39] S. Maji, A. Berg, and J. Malik, "Classification Using Intersection Kernel Support Vector Machines Is Efficient," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[40] L. Marchesotti, C. Cifarelli, and G. Csurka, "A Framework for Visual Saliency Detection with Applications to Image Thumbnailing," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
[41] K. Mikolajczyk and C. Schmid, "Scale & Affine Invariant Interest Point Detectors," Int'l J. Computer Vision, vol. 1, no. 60, pp. 63-86, 2004.
[42] F. Moosmann, D. Larlus, and F. Jurie, "Learning Saliency Maps for Object Categorization," Proc. European Conf. Computer Vision, 2006.
[43] A. Neubeck and L. Van Gool, "Efficient Non-Maximum Suppression," Proc. 18th Int'l Conf. Pattern Recognition, 2006.
[44] E. Parzen, "On the Estimation of a Probability Density Function," Annals of Math. Statistics, vol. 33, no. 3, pp. 1065-1076, 1962.
[45] A. Prest, C. Schmid, and V. Ferrari, "Weakly Supervised Learning of Interactions between Humans and Objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 601-614, Mar. 2012.
[46] B.C. Russell, A.A. Efros, J. Sivic, W.T. Freeman, and A. Zisserman, "Using Multiple Segmentations to Discover Objects and Their Extent in Image Collections," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[47] P. Siva and T. Xiang, "Weakly Supervised Object Detector Learning with Model Drift Detection," Proc. IEEE Int'l Conf. Computer Vision, 2011.
[48] J. Sun and H. Ling, "Scale and Object Aware Image Retargeting for Thumbnail Browsing," Proc. IEEE Int'l Conf. Computer Vision, 2011.
[49] R. Valenti, N. Sebe, and T. Gevers, "Image Saliency by Isocentric Curvedness and Color," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
[50] A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, "Multiple Kernels for Object Detection," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
[51] A. Vedaldi and A. Zisserman, "Efficient Additive Kernels via Explicit Feature Maps," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[52] A. Vezhnevets, V. Ferrari, and J.M. Buhmann, "Weakly Supervised Semantic Segmentation with Multi Image Model," Proc. IEEE Int'l Conf. Computer Vision, 2011.
[53] G. Wang and D. Forsyth, "Joint Learning of Visual Attributes Object Classes and Visual Saliency," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
8 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool